RegularizedEnsembleRegressor

Struct RegularizedEnsembleRegressor 

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pub struct RegularizedEnsembleRegressor { /* private fields */ }
Expand description

Regularized ensemble regressor with L1/L2 regularization

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impl RegularizedEnsembleRegressor

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pub fn new(config: RegularizedEnsembleConfig) -> Self

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pub fn builder() -> RegularizedEnsembleRegressorBuilder

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pub fn get_ensemble_weights(&self) -> &[f64]

Get ensemble weights

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pub fn get_regularization_path(&self) -> &[RegularizationStep]

Get regularization path

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pub fn get_sparsity(&self) -> f64

Get sparsity level (fraction of zero weights)

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impl Estimator for RegularizedEnsembleRegressor

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type Config = RegularizedEnsembleConfig

Configuration type for the estimator
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type Error = SklearsError

Error type for the estimator
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type Float = f64

The numeric type used by this estimator
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fn config(&self) -> &Self::Config

Get estimator configuration
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fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context
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fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions
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fn metadata(&self) -> EstimatorMetadata

Get estimator metadata
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impl Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, Vec<f64>> for RegularizedEnsembleRegressor

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type Fitted = RegularizedEnsembleRegressor

The fitted model type
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fn fit(self, X: &Array2<f64>, y: &Vec<f64>) -> SklResult<Self::Fitted>

Fit the model to the provided data with validation
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fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>
where Self: Sized,

Fit with custom validation and early stopping
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impl Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, Vec<f64>> for RegularizedEnsembleRegressor

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fn predict(&self, X: &Array2<f64>) -> SklResult<Vec<f64>>

Make predictions on the provided data
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fn predict_with_uncertainty( &self, x: &X, ) -> Result<(Output, UncertaintyMeasure), SklearsError>

Make predictions with confidence intervals

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const STABLE_SINCE: &'static str = "0.1.0"

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impl<T, C, E, F, X, Y> MultiTaskEstimator<C, E, F, X, Y> for T
where T: Estimator<Config = C, Error = E, Float = F> + Predict<X, Y>,